Fuzzy Logic Control with the Intel 8xc196 Embedded Microcontroller

Fuzzy logic control is being increasingly applied to solve control problems in areas where system complexity, development time and cost are the major issues. In the absence of a system mathematical model , a fuzzy system model is described which is analogous to a human operator’s behavior, based on approximate reasoning bound by a minimum set of rules. A set of linguistic fuzzy control rules are set up which are conditional linguistic statements which establish the relationships between the inputs and the outputs. The fuzzy system is associated with Binary Input Output Fuzzy Associative Memories which are used for control. This paper discusses the development of a fuzzy inference unit and algorithms for fuzzification, rule evaluation and defuzzification of a fuzzy closed loop control system. Tools and techniques to generate optimized fuzzy based real time code in assembly and C, with short development time, are shown for the Intel 8XC196 microcontroller. Performance and features of the 80C196 for fuzzy-based control are analyzed. INTRODUCTION TO FUZZY LOGIC Fuzzy logic is being increasingly used over a wide range of areas such as industrial control, image processing, auto industry as well as commercial products and has become one of the most popular and successful methods used to design and implement complex control systems. Classical controllers are designed by various techniques for a variety of control systems applications and are modeled on the systems or process being controlled. Fuzzy control is based on the human operator's behavior. Control rules in the case of an anti-lock braking system (ABS) of an automobile may include variables such as the car speed, brake pressure, brake temperature, time of brake application and many other variables which are continuous in nature and the range of these variables are set by the designer. Fuzzy logic easily represents continuous data in digital computers. The linguistic variable, speed may adopt values such as small, positive small, large, negative large and so on. By applying an IF-THEN rule fuzzy theory is applied to real time control i.e.; "if speed is slow then brake pressure is negative small", hence a single fuzzy rule replaces conventional rules. Fuzzy logic is used to build a controller even when a mathematical model of a system does not exist or is incomplete and creates a control surface by combining rules and fuzzy sets. Fuzzy Associative Memories (FAM) are transformations which map fuzzy sets to fuzzy sets. A FAM system maps antecedents to consequents and is a collection of IF-THEN rules. The universes of discourse for the fuzzy controller are pre-defined and each universe of discourse contains a library of fuzzy sets. Each fuzzy set allows its members to have different grades of membership, each expressed by a number in the interval [0,1], adjacent sets in the universe of discourse overlap. A weighted quantization of antecedent X and the consequent Y are sets in the library which includes the fuzzy set value that the fuzzy variable can have. Defuzzification schemes in the form of centroid defuzzification are used to directly compute the real